Re: [agi] Ben vs. the AI academics...
Yeah, it was fun to watch you stir them up, Ben. But they did take you seriously in the discussions, for example when they included your provocative quote in the plenary summary. A lot of the systems had impressive behavior, but most were dead end approaches, in my opinion, because they made logical reasoning fundamental with learning as an add-on. The most impressive talk from the main stream AI community was by Deb Roy, who is achieving interesting vision-language coordination with systems that are fundamentally about learning. It was good to see Ben again, and to meet Moshe Looks and Pei Wang. Cheers, Bill On Sat, 23 Oct 2004, Ben Goertzel wrote: Hmmm... I just had a somewhat funny experience with the traditional AI research community Moshe Looks and I gave a talk Friday at the AAAI Symposium on Achieving Human-Level Intelligence Through Integrated Systems and Research. Our talk was an overview of Novamente; if you're curious our conference paper is at http://www.realai.net/AAAI04.pdf Anyway, I began my talk by noting that, in my opinion, Seeking human-level intelligence is not necessarily the best approach to AI. We humans aren't all that smart anyway, in the grand scheme of things; and it may be that the best approach to superintelligence doesn't even pass through humanlike intelligence, since human wetware is pretty different from computer hardware. Wow, did that piss off the audience!! (an audience which, as I later found out, consisted largely of advocates of the SOAR and ACT-R cognitive modeling systems, which seek to model human cognition in detail, not by modeling human brain function but via tuning various logic and search algorithms to have similar properties to human cognition.) Moshe and I went on to give a talk on Novamente, which was hard to do because we (like many others who were accepted for the symposium but not part of the AAAI inner circle) were allocated only 12 minutes plus 3 minutes for questions (Of course, it's not hard to summarize Novamente at a certain level of abstraction in 12 minutes, but it's pretty much impossible to be at all *convincing* to skeptical AI experts in that time-frame.) So far as I could tell, no one really understood much of what we were talking about -- because they were so irritated at me for belittling humanity, and because the Novamente architecture is too different from the usual for these guys to really understand it from such a compressed presentation. After our talk, one of the more esteemed members of the audience irritatedly asked me how I knew human intelligence wasn't the maximal possible intelligence -- had I actually experienced superior intelligences myself? I was tempted to refer him to Terrence McKenna and his superintelligent 9-dimensional machine-elves, but instead I just referred to computation theory and the obvious limitations of the human brain. Then he asked whether our system actually did anything, and I mentioned the Biomind and language-processing applications, which seemed to surprise him even though we had just talked about them in our prsentation. Most of the talks on Friday and Saturday were fairly unambitious, though some of them were interesting technically -- the only other person presenting a real approach to human-level intelligence, besides me and Moshe, was Pei Wang. Nearly all of the work presented was from a logic-based approach to AI. Then there were some folks who posited that logic is a bad approach and AI researchers should focus entirely on perception and action, and let cognition emerge directly from these. Then someone proposed that if you get the right knowledge representation, human-level AI is solved and you can use just about any algorithms for learning and reasoning, etc. In general I didn't think the discussion ever dug into the really deep and hard issues of achieving human-level AI, though it came close a couple times. For instance, there was a talk describing work using robot vision and arm-motion to ground linguistic concepts -- but it never got beyond the trivial level of using supervised categorization to ground particular words in sets of pictures, or using preprogrammed arm-control schema triggered by the output of a language parser in preprogrammed ways.. There was a lot of talk about how hard it is for academics to get funding for academic research aimed at human-level AI, and tomorrow morning's session (which I plan to skip -- better to stay home and work on Novamente!) will include some brainstorming on how to improve this situation gradually over the next N years. It seemed that the only substantial funding source for the work presented in the symposium was DARPA. Then, Sat. night, there was a session in which the people from our symposium got together with the people from the 5 other AAAI symposia being held in the same hotel. One member from each symposium was supposed to get up and give a
p.s., Re: [agi] Ben vs. the AI academics...
My talk is available at: http://www.ssec.wisc.edu/~billh/g/FS104HibbardB.pdf There was a really interesting talk by the neuroscientist Richard Grainger with some publications available at: http://www.brainengineering.com/publications.html Cheers, Bill --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
A lot of the systems had impressive behavior, but most were dead end approaches, in my opinion, because they made logical reasoning fundamental with learning as an add-on. The most impressive talk from the main stream AI community was by Deb Roy, who is achieving interesting vision-language coordination with systems that are fundamentally about learning. It was good to see Ben again, and to meet Moshe Looks and Pei Wang. Cheers, Bill Hey Bill -- Just a brief comment about Deb Roy's work., then some more comments on human-level AI in general. Deb's work is really interesting, however it actually represents a move AWAY from learning, as compared to his thesis work a few years ago. Then he was focusing on having his software system learn visuomotor groundings for linguistic terms (nouns like apple, etc.). Now he has been making his system do more complex stuff, but via hard-coding control schema into it, rather than via learning. When Moshe and I talked to him after his talk, however, he said his next step would be to implement some approach to learning these control schemata. But he didn't seem to have such a clear idea about how he'd do it. When Moshe asked him about how he's implement grounding of prepositional and subject-argument relationships (arguably the nontrivial part of language-grounding), he said he didn't have an approach to that yet because he didn't know any good way to represent that kind of knowledge on the cognitive level. So, I think his work has tremendous promise; but yet, I couldn't help wish he'd pushed it in a more learning-based direction during the last few years. On the other hand, I can sympathize, because -- for instance -- over the last year we've had a Novamente team member (Mike Ross) create a hard-coded language processing module. Why? Because we needed it for a commercial project. (Deb Roy is an academic -- academics don't have revenue pressures, but they often have demo pressures associated with funding sources!). Now, during 2005 Mike will replace this hard-coded language processing module with a learning-oriented language processing module. Basically, the need for incremental useful results can be a burden. It's good because it keeps you from moving a long way in a useless direction, but it can also tremendously slow down progress toward long-term goals. Earlier this year, in the US Virgin Islands, Marvin Minsky and his friends had a private AI symposium on the topic of human-level intelligence. It was written in up the June 2004 issue of the AI Magazine, and it seems to have been a bit more interesting than the AAAI symposium that we just attended. No real solutions were proposed, though; the focus was on Minsky's and Sloman's architectures for human-level AI (e.g. Minsky's Emotion Machine stuff). One idea proposed by Minsky at that conference is something I disagree with pretty radically. He says that until we understand human-level intelligence, we should make our theories of mind as complex as possible, rather than simplifying them -- for fear of leaving something out! This reminds me of some of the mistakes we made at Webmind Inc. I believe our approach to AI there was fundamentally sound, yet the theory underlying it (not the philosophy of mind, but the intermediate level computational-cog-sci theory) was too complex which led to a software system that was too large and complex and hard to maintain and tune. Contra Minsky and Webmind, in Novamente I've sought to create the simplest possible design that accounts for all the diverse phenomena of mind on an emergent level. Minsky is really trying to jam every aspect of the mind into his design on the explicit level. Another idea that came up at the Virgin Islands symposium was to create a simulation world in which AI systems control agents that collectively try to solve simple object-manipulation tasks. The prototype case is a bunch of kids collaborating to build towers out of blocks. The idea was also raised of making the simulation more realistic by making the block-building take place in a simulated livingroom or restaurant or some such. I like this line of thinking because it is closely related to the AGI-SIM simulation world project that we're currently working on (an open-source sim-world to be used for Novamente bue also by other projects). -- Ben --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
On Sun, 24 Oct 2004, Ben Goertzel wrote: One idea proposed by Minsky at that conference is something I disagree with pretty radically. He says that until we understand human-level intelligence, we should make our theories of mind as complex as possible, rather than simplifying them -- for fear of leaving something out! This reminds me of some of the mistakes we made at Webmind Inc. I believe our approach to AI there was fundamentally sound, yet the theory underlying it (not the philosophy of mind, but the intermediate level computational-cog-sci theory) was too complex which led to a software system that was too large and complex and hard to maintain and tune. Contra Minsky and Webmind, in Novamente I've sought to create the simplest possible design that accounts for all the diverse phenomena of mind on an emergent level. Minsky is really trying to jam every aspect of the mind into his design on the explicit level. Can you provide a quote from Minsky about this? That's certainly an interesting position to take. The entire field of cognitive psychology is intent on reducing the complexity of its own function so that it can be understood by itself. On the other hand, Minsky's point is probably more one of evolutionary progress across the entire field, we should try many avenues and select those that work best, rather than getting locked into narrow visions of how the brain works as has happened repeatedly throughout the history of Psychology. Re: Deb, his stuff is clearly an amazing accomplishment, although I think that his success is more of a technical than a deeply theoretical flavor. On a more general note, I wouldn't expect to impress the AI community with just your theories and ideas. There are many AI frameworks out there, and it takes too much effort to understand new ones that come along until they do something amazing. So you'll need a truly impressive demo to make a splash. Until you do that, every AI conference you go to will be like this one. Deb's learned this lesson and learned it well :) -Brad --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
Hi Brad, Of course I understand that to get the academic community (or anyone else) really excited about Novamente as an AGI system, we'll need splashy demos. They will come in time, don't worry ;-) We have specifically chosen to develop Novamente in accordance with a solid long-term design, rather than with a view toward creating splashy short-term demos. When we have taken short-cuts it has been in order to get the system to do commercially useful things for generating revenue, rather than to make splashy demos for the academic community. And, I hope my comments didn't seem to be dissing Deb Roy's work. It's really good stuff, and was among the more interesting stuff at this conference, for sure. I don't fault the academic AI community for not being psyched about Novamente, which is unproven. I do fault them for such things as * still being psyched about SOAR and ACT-R, which have been around for decades and have proved both theoretically and pragmatically very sorely limited * foolishiness such as Psychometric AI, which posits fairly trivial puzzle-solving achievements as supposed progress toward human-level AI (I note that Selmer Bringsjord is a very smart guy with some great research achievements; I just don't think his Psychometric AI idea is one of them...) * being psyched about clearly impractical architectures like Minsky's Emotion Machine, which is even more unproven than Novamente (unlike him, we do have a partially-complete software system that does some useful stuff), and seems unimplementable in principle due to its over-complexity Regarding Minsky, a quote from p. 118 of AI Magazine Summer 2004 is: Minsky responded by arguing that today, when our theories still explain too little, we should elaborate rather than simplify, and we should be building theories with more parts, not fewer. This general philosophy pervades his architectural design, with its many layers, representations, critics, reasoning methods and other diverse types of components. Only once we have built an architecture rich enough to explain most of what people can do will it make sense to try and simplify things. But today, we are still far from an architecture that explains even a tiny fraction of human cognition. Now, I understand well that the human brain is a mess with a lot of complexity, a lot of different parts doing diverse things. However, what I think Minsky's architecture does is to explicitly embed, in his AI design, a diversity of phenomena that are better thought of as being emergent. My argument with him then comes down to a series of detailed arguments as to whether this or that particular cognitive phenomenon a) is explicitly encoded or emergent in human cognitive neuroscience b) is better explicitly encoded, or coaxed to emerge, from an AI system In each case, it's a judgment call, and some cases are better understood based on current AI or neuroscience knowledge than others. But I think Minsky has a consistent, very strong bias toward explicit encoding. This is the same kind of bias underlying Cyc and a lot of GOFAI. For instance, Minsky's architecture contains a separate component dealing with Self-Ideals: assessing one's activities with respect to the ideals established via interactions with one's role models. I don't think this should be put into one's AI system via drawing a little box around it with a connector going to other components. Rather, this seems to me like something that should emerge from lower-level social and cognitive and motivational components and dynamics. -- Ben G -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Brad Wyble Sent: Sunday, October 24, 2004 11:05 AM To: [EMAIL PROTECTED] Subject: RE: [agi] Ben vs. the AI academics... On Sun, 24 Oct 2004, Ben Goertzel wrote: One idea proposed by Minsky at that conference is something I disagree with pretty radically. He says that until we understand human-level intelligence, we should make our theories of mind as complex as possible, rather than simplifying them -- for fear of leaving something out! This reminds me of some of the mistakes we made at Webmind Inc. I believe our approach to AI there was fundamentally sound, yet the theory underlying it (not the philosophy of mind, but the intermediate level computational-cog-sci theory) was too complex which led to a software system that was too large and complex and hard to maintain and tune. Contra Minsky and Webmind, in Novamente I've sought to create the simplest possible design that accounts for all the diverse phenomena of mind on an emergent level. Minsky is really trying to jam every aspect of the mind into his design on the explicit level. Can you provide a quote from Minsky about this? That's certainly an interesting position to take. The entire field of cognitive psychology is intent on reducing the complexity of its own function so that it can
RE: [agi] Ben vs. the AI academics...
Now, I understand well that the human brain is a mess with a lot of complexity, a lot of different parts doing diverse things. However, what I think Minsky's architecture does is to explicitly embed, in his AI design, a diversity of phenomena that are better thought of as being emergent. My argument with him then comes down to a series of detailed arguments as to whether this or that particular cognitive phenomenon a) is explicitly encoded or emergent in human cognitive neuroscience b) is better explicitly encoded, or coaxed to emerge, from an AI system A not incidental point here is that Minsky's design lacks any learning dynamics that could possibly lead to anything emerging. I had an argument with Minsky about this in the late 90's, and he basically told me he thought the notion of emergence as applied to cognitive systems was a crock of nonsense... Basically, the people at this human-level AAAI symposium seemed divided into: * those who agree with Minsky that cognitive emergence is a crock * those who think that cognition emerges entirely from perception and action Complex, self-organizing dynamics of cognition is a foreign concept, a kind of gibberish, to most [of course, not all] of these folks ;-) -- Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
Hi Brad, really excited about Novamente as an AGI system, we'll need splashy demos. They will come in time, don't worry ;-) We have specifically chosen to Looking forward to it as ever :) I can understand your frustration with this state of affairs. Getting people to buy into your theoretical framework requires a major time investment on their part. This is why my own works stays within the bounds of conventional experimental and psychological research. I speak the same langauge as everyone else, and so it's easy to cross pollenate ideas. Of course, this is also why SOAR and similar architectures have such appeal despite their limitations. Because the SOAR community is speaking the same language to one another, it's possible (in theory) for the whole of them to make faster progress than if they each had their own pet architechture. This synergy is very real, but may be outweighed by SOAR's limitations. And, I hope my comments didn't seem to be dissing Deb Roy's work. It's really good stuff, and was among the more interesting stuff at this conference, for sure. Not at all, I think we're in general agreement about the value of his work. Now, I understand well that the human brain is a mess with a lot of complexity, a lot of different parts doing diverse things. However, what I think Minsky's architecture does is to explicitly embed, in his AI design, a diversity of phenomena that are better thought of as being emergent. My argument with him then comes down to a series of detailed arguments as to whether this or that particular cognitive phenomenon a) is explicitly encoded or emergent in human cognitive neuroscience b) is better explicitly encoded, or coaxed to emerge, from an AI system In each case, it's a judgment call, and some cases are better understood based on current AI or neuroscience knowledge than others. But I think Minsky has a consistent, very strong bias toward explicit encoding. This is the same kind of bias underlying Cyc and a lot of GOFAI. Whether something is explicit or emergent depends only on your perspective of what counts as explicit. I'll assume you mean anatomically explicit in some way (where anatomical refers to features of both neurophysiology and box/arrow design). With this assumption, I think b follows from a. Evolution has always looked for the efficient solution, so if evolution has explicitly encoded these behaviors, it's likely the best way to do it, at least as far as we'll be able to determine with our stupid human brains :) There's certainly a huge preponderance of evidence that our brains have leaned towards specific anatomically explicit solutions to problems in the domains that we can examine easily (near the motor and sensory areas). Of course, in many cases these anatomically explicity solutions are emergent from developmental processes, but I still think they should be considered explicit. -Brad --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
Hi, Looking forward to it as ever :) I can understand your frustration with this state of affairs. Getting people to buy into your theoretical framework requires a major time investment on their part. This is why my own works stays within the bounds of conventional experimental and psychological research. I speak the same langauge as everyone else, and so it's easy to cross pollenate ideas. Of course, this is also why SOAR and similar architectures have such appeal despite their limitations. Because the SOAR community is speaking the same language to one another, it's possible (in theory) for the whole of them to make faster progress than if they each had their own pet architechture. Yes, this issue of specialized languages is a hard problem for AGI work. This is one reason that, when hiring people for Novamente projects, I have a bias toward former Webmind-ers Even though Novamente is a quite different software system and mathematical framework from Webmind, it's based on the same sort of conceptual language, and the folks who worked at Webmind are used to that language. I noticed at this conference that different researchers were using basic words like knowledge and representation and learning and evolution in very different ways -- which makes communication tricky! When Pei Wang and I worked together in 1998-2001, we spent about a month initially just establishing a common language in which we could communicate to really understand what our agreements and disagreements were... Whether something is explicit or emergent depends only on your perspective of what counts as explicit. I'll assume you mean anatomically explicit in some way (where anatomical refers to features of both neurophysiology and box/arrow design). In an AI context, it means whether something exists explicitly in the source code, rather than coming about dynamically as an indirect result of the sourcecode, in the bit-patterns in RAM created by the executable running... With this assumption, I think b follows from a. Evolution has always looked for the efficient solution, so if evolution has explicitly encoded these behaviors, it's likely the best way to do it, at least as far as we'll be able to determine with our stupid human brains :) There's certainly a huge preponderance of evidence that our brains have leaned towards specific anatomically explicit solutions to problems in the domains that we can examine easily (near the motor and sensory areas). Of course, in many cases these anatomically explicity solutions are emergent from developmental processes, but I still think they should be considered explicit. Agreed. And I think that sensorimotor stuff is more likely to be explicit rather than emergent in the brain And that, in coding an AI system, it's hopeless to try to make too much of cognition explicit rather than emergent -- but the same statement probably doesn't hold for perception action... -- Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Ben vs. the AI academics...
So much for getting work done today :) I noticed at this conference that different researchers were using basic words like knowledge and representation and learning and evolution in very different ways -- which makes communication tricky! Don't get me started on Working Memory. In an AI context, it means whether something exists explicitly in the source code, rather than coming about dynamically as an indirect result of the sourcecode, in the bit-patterns in RAM created by the executable running... A fair definition. Agreed. And I think that sensorimotor stuff is more likely to be explicit rather than emergent in the brain And that, in coding an AI system, it's hopeless to try to make too much of cognition explicit rather than emergent -- but the same statement probably doesn't hold for perception action... If that were the case, would you not expect to see more variance in high level behaviors? Instead we tend to see the same types of behavior expressed, the only difference between people being the relative amount of expression of these tendencies. But I guess that's an arguable point, whether these observed tendencies among a population of people are actually there, or are only a product of the theories used to classify them. -Brad --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Ben vs. the AI academics...
I just got home and have no time to write long emails --- I type much slower then Ben does. ;-) I'm very glad to meet Ben again, and Bill and Moshe for the first time (as well as some other people who are not in this list). The Symposium description and schedule can be found at http://xenia.media.mit.edu/~nlc/conferences/fss04.html, and it won't be hard to find the homepage of a speaker if one talk sounds interesting. A preprint version of my paper is at http://www.cis.temple.edu/~pwang/drafts/PeiWang-FSS04.pdf. Pei - Original Message - From: Ben Goertzel [EMAIL PROTECTED] To: [EMAIL PROTECTED] Org [EMAIL PROTECTED]; [EMAIL PROTECTED] Listbox. Com [EMAIL PROTECTED] Sent: Saturday, October 23, 2004 10:05 PM Subject: [agi] Ben vs. the AI academics... Hmmm... I just had a somewhat funny experience with the traditional AI research community Moshe Looks and I gave a talk Friday at the AAAI Symposium on Achieving Human-Level Intelligence Through Integrated Systems and Research. Our talk was an overview of Novamente; if you're curious our conference paper is at http://www.realai.net/AAAI04.pdf Anyway, I began my talk by noting that, in my opinion, Seeking human-level intelligence is not necessarily the best approach to AI. We humans aren't all that smart anyway, in the grand scheme of things; and it may be that the best approach to superintelligence doesn't even pass through humanlike intelligence, since human wetware is pretty different from computer hardware. Wow, did that piss off the audience!! (an audience which, as I later found out, consisted largely of advocates of the SOAR and ACT-R cognitive modeling systems, which seek to model human cognition in detail, not by modeling human brain function but via tuning various logic and search algorithms to have similar properties to human cognition.) Moshe and I went on to give a talk on Novamente, which was hard to do because we (like many others who were accepted for the symposium but not part of the AAAI inner circle) were allocated only 12 minutes plus 3 minutes for questions (Of course, it's not hard to summarize Novamente at a certain level of abstraction in 12 minutes, but it's pretty much impossible to be at all *convincing* to skeptical AI experts in that time-frame.) So far as I could tell, no one really understood much of what we were talking about -- because they were so irritated at me for belittling humanity, and because the Novamente architecture is too different from the usual for these guys to really understand it from such a compressed presentation. After our talk, one of the more esteemed members of the audience irritatedly asked me how I knew human intelligence wasn't the maximal possible intelligence -- had I actually experienced superior intelligences myself? I was tempted to refer him to Terrence McKenna and his superintelligent 9-dimensional machine-elves, but instead I just referred to computation theory and the obvious limitations of the human brain. Then he asked whether our system actually did anything, and I mentioned the Biomind and language-processing applications, which seemed to surprise him even though we had just talked about them in our prsentation. Most of the talks on Friday and Saturday were fairly unambitious, though some of them were interesting technically -- the only other person presenting a real approach to human-level intelligence, besides me and Moshe, was Pei Wang. Nearly all of the work presented was from a logic-based approach to AI. Then there were some folks who posited that logic is a bad approach and AI researchers should focus entirely on perception and action, and let cognition emerge directly from these. Then someone proposed that if you get the right knowledge representation, human-level AI is solved and you can use just about any algorithms for learning and reasoning, etc. In general I didn't think the discussion ever dug into the really deep and hard issues of achieving human-level AI, though it came close a couple times. For instance, there was a talk describing work using robot vision and arm-motion to ground linguistic concepts -- but it never got beyond the trivial level of using supervised categorization to ground particular words in sets of pictures, or using preprogrammed arm-control schema triggered by the output of a language parser in preprogrammed ways.. There was a lot of talk about how hard it is for academics to get funding for academic research aimed at human-level AI, and tomorrow morning's session (which I plan to skip -- better to stay home and work on Novamente!) will include some brainstorming on how to improve this situation gradually over the next N years. It seemed that the only substantial funding source for the work presented in the symposium was DARPA. Then, Sat. night, there was a session in which the people from our symposium got together with the people from the 5 other AAAI symposia being held in the same hotel. One member from each symposium
Re: [agi] Ben vs. the AI academics...
One idea proposed by Minsky at that conference is something I disagree with pretty radically. He says that until we understand human-level intelligence, we should make our theories of mind as complex as possible, rather than simplifying them -- for fear of leaving something out! This reminds me of some of the mistakes we made at Webmind Inc. I believe our approach to AI there was fundamentally sound, yet the theory underlying it (not the philosophy of mind, but the intermediate level computational-cog-sci theory) was too complex which led to a software system that was too large and complex and hard to maintain and tune. Contra Minsky and Webmind, in Novamente I've sought to create the simplest possible design that accounts for all the diverse phenomena of mind on an emergent level. Minsky is really trying to jam every aspect of the mind into his design on the explicit level. Can you provide a quote from Minsky about this? That's certainly an interesting position to take. The entire field of cognitive psychology is intent on reducing the complexity of its own function so that it can be understood by itself. The AI magazine paper is on-line available at The St. Thomas common sense symposium: designing architectures for human-level intelligence (http://web.media.mit.edu/~push/StThomas-AIMag.pdf) Pei --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
